Measuring city-scale green infrastructure drawdown dynamics using internet-connected sensors in Detroit

TL;DR

This study introduces a novel wireless sensor network and data analysis approach to measure and model green infrastructure water drawdown dynamics at city scale, providing insights into key factors influencing GI performance.

Contribution

It presents a new wireless sensor network and automated storm analysis method to quantify GI drawdown dynamics and identify key physiographic features affecting performance.

Findings

Water levels measured at 14 sites in Detroit from June to September 2021.

Drawdown rate correlates with groundwater depth, imperviousness, and drainage features.

Modeling reveals physiographic features significantly influence GI effectiveness.

Abstract

The impact of green infrastructure (GI) on the urban drainage landscape remains largely unmeasured at high temporal and spatial scales. To that end, a data toolchain is introduced, underpinned by a novel wireless sensor network for continuously measuring real-time water levels in GI. The internet-connected sensors enable the collection of high-resolution data across large regions. A case study in Detroit (MI, US) is presented, where the water levels of 14 GI sites were measured in-situ from June to September 2021. The large dataset is analyzed using an automated storm segmentation methodology, which automatically extracts and analyzes individual storms from measurement time series. Storms are used to parameterize a dynamical system model of GI drawdown dynamics. The model is completely described by the decay constant {\alpha}, which is directly proportional to the drawdown rate. The parameter is analyzed across storms to compare GI dynamics between sites and to determine the major design and physiographic features that drive drawdown dynamics. A correlation analysis using Spearman's rank correlation coefficient reveals that depth to groundwater, imperviousness, longitude, and drainage area to surface area ratio are the most important features explaining GI drawdown dynamics in Detroit. A discussion is provided to contextualize these finding and explore the implications of data-driven strategies for GI design and placement.